Density-based clustering

Ricardo J.G.B. Campello, Peer Kröger, Jörg Sander, Arthur Zimek*

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Clustering refers to the task of identifying groups or clusters in a data set. In density-based clustering, a cluster is a set of data objects spread in the data space over a contiguous region of high density of objects. Density-based clusters are separated from each other by contiguous regions of low density of objects. Data objects located in low-density regions are typically considered noise or outliers. In this review article we discuss the statistical notion of density-based clusters, classic algorithms for deriving a flat partitioning of density-based clusters, methods for hierarchical density-based clustering, and methods for semi-supervised clustering. We conclude with some open challenges related to density-based clustering. This article is categorized under: Technologies > Data Preprocessing Ensemble Methods > Structure Discovery Algorithmic Development > Hierarchies and Trees.

TidsskriftWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Udgave nummer2
Antal sider15
StatusUdgivet - apr. 2020